A Systematic Literature Network Analysis: Research Mapping of International Roughness Index
Bibliographic record
Abstract
The International Roughness Index (IRI) is a widely used measure of pavement roughness that has important implications for vehicle safety, ride quality, and road maintenance.Over the years, many research studies have been conducted on IRI, but the literature is dispersed and lacks an overall research mapping.To address this issue, a Systematic Literature Network Analysis (SLNA) method to review the topic of IRI from scientific publications from 2000-2021 to obtain state-of-the-art mapping, trending topics, and future work projection.The results show a significant increase in scientific document publications.Network visualization contains 189 keywords divided into six clusters.The biggest cluster is focuses on measuring road surface conditions to obtain the IRI value as part of monitoring road surface conditions using a mechanical method and vibration response.The keywords featured on the word cloud are pavements, surface roughness, road and street, pavement performance, asphalt pavement, and concrete pavement.Top trend topics are predictive analytics, decision trees, machine learning, and roughness prediction.The keywords machine learning and learning algorithms are up-to-date topics and closely related to forecasting and the international roughness index.The IRI prediction model is still feasible for further research by using a machine learning prediction model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.081 | 0.065 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".